1. Field of the Invention
This invention generally relates to a vehicle hybrid power system and method for creating simulated equivalent fuel consumption multidimensional data applicable thereto, and, more specifically, to a hybrid power system that is capable of optimizing the system control strategy by applying its created simulated equivalent fuel consumption multidimensional data, and further controlling actions over subsystems, such as the engine, motor, clutch, generator, and others.
2. Description of Related Art
In response to soaring international oil prices and the fact that environmental concerns in a time of increasing global awareness are leading to demands for optimal fuel consumption, the automobile industry is aggressively developing vehicles possessing the benefits of reduced energy consumption, reduced pollution, and cleaner power sources. Moreover, in that motors are characterized with high torque at low rotation speeds and in that engines are characterized with high efficiency and low pollution at high rotation speeds, the automobile industry is pursuing the development of hybrid power systems that utilizes both motors and engines operating together or separately to provide optimal movement and operation of vehicles.
Control over the operational mode of a hybrid power system can generally be achieved by combining engineering experience and theoretical analysis. Utilizing engineering experience, control can be achieved by directly studying stability performance diagrams for the comprised subsystems, such as the engine, motor, battery, and then deciding the appropriate work zone for each of the power sources available as well as defining the switching conditions used to transition from one power source to another. FIG. 1a is a contour diagram of the brake specific fuel consumption (BSFC) of an engine, which is a measure of an engine's efficiency calculated by dividing the fuel consumption rate by the generated power. In the figure, the oval area is the preferred operating zone in terms of engine efficiency, wherein identically-shaped power efficiency curves P1 and P2 are determined at tangent points on the preferred efficiency zone. When output power efficiency is below power efficiency curve P2, a hybrid vehicle's electrical motor is employed as the vehicle's power source because an engine has poor rotational efficiency under this situation. When output power efficiency falls between the power efficiency curves P1 and P2, a hybrid vehicle's engine is employed as the vehicle's power source because the engine has preferable rotational efficiency under this situation. Lastly, when output power efficiency is above the power efficiency curve P1, both the engine and the electrical motor are employed as the vehicle power source, thereby outputting higher power efficiency. As shown in FIG. 1b, which is a relation diagram of equal efficiency while a motor is operating, the preferable state of motor operation is illustrated as equal efficiency curve E1. However, in regards to FIGS. 1a and 1b, engineering experience defines the operational zones of each subsystem based on only direct engineering observation rather than overall analysis. Therefore, this method is incapable of providing a holistic relation among each of the comprised subsystems of a vehicle. For example, if the motor is selected as the power source based on the data shown in FIG. 1a, the rotation speed and torque of the motor are not necessarily in the optimal state for motor operation as shown in FIG. 1b. 
Therefore, when engineering experience is applied to a sophisticated system, a tiresome process of trial-and-error must keep testing and adjusting system parameters or work zones of subsystems, and the results derived from the process of trial-and-error are only local optimizations. In other words, direct observation of engineering experience is incapable of providing a sophisticated hybrid power system with a holistic optimization control strategy over each of various subsystems.
Another method to design a hybrid power system is by theoretical analysis. Using this method, a vehicle model must be built using mathematical theory. However, since the methods resulting from theoretical analysis are generally complicated and computationally intensive, such theoretical analysis cannot easily be applied to a vehicle in real-time for the purpose of operational control. Instead, a simplified model must be developed by first using off-line analysis, and then determining a rule-based control system after observing or inducting performance at each operational point. Then, system parameters and work zones for various subsystems can be adjusted based on this rule-based control system. The techniques utilized to implement the models resulting from such theoretical analysis are based on, for example, curve-fitting, “if-then-else” constructs, flowcharts, neural networks, and fuzzy logic, to name some of the more common techniques.
With regard to vehicle control, a prior applied method of theoretical analysis involves first imputing data for a vehicle and related driving test data. Next, build a first-approximation vehicle model, and then input theoretical controls into the vehicle model for dynamic analysis to obtain evaluation results. Then, transfer the evaluation results to a kinetic distribution diagram by dot-plotting, and, after that, an operational curve can be derived from the kinetic distribution diagram by curve fitting, thereby achieving the objective of “normalization.” Lastly, the results from the normalization process are then copied to or implemented in a vehicle control unit (VCU), thus completing the on-line (real-time) control strategy.
However, a power system for a hybrid vehicle is a complex system that integrates various subsystems, such as the engine, motor, battery/generator, and transmission mechanisms. Therefore, such a huge typically requires a long time to build its model, and if a precise evaluation of the system dynamics is expected, such a system model should be one of higher order. Likewise, the efficacy of such a system model cannot be proven until much experimental comparison has been done. Moreover, in the subsequent processes of analyzing and drafting the control strategy, since simulated analysis of a higher-order vehicle model generally takes a relatively long period of time, in practice, only the first order vehicle model is applied to simulated analysis for the purpose of greatly speeding up the simulated operations. However, since a first order vehicle model cannot closely represent the actual state of a vehicle actual, the results derived from such a simulated analysis based on a first order vehicle model have low precision, and the control strategy determined by the result has low accuracy or reliability as a consequence.
Hence, it is a highly urgent issue in the industry to provide a technique that is capable of determining optimized control parameters for hybrid vehicle operational control, and further controlling operation of the engine, motor, clutch, generator, etc. based on such parameters, consequently achieving the objective of reducing system fuel consumption to a minimum and effectively solving the drawbacks of the prior arts, in which trial-and-error engineering experience and first-order theoretical analysis cannot effectively establish an optimized control strategy.